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Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年第11期25卷 19613-19631页

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China North University of China institute for Big Data and Visual Computing Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method is proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

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OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models 62

OOP: Object-Oriented Programming Evaluation Benchmark for La...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Wang, Shuai Ding, Liang Shen, Li Luo, Yong Du, Bo Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Luojia Laboratory Wuhan China The University of Sydney Australia School of Cyber Science and Teachnology Sun Yat-sen University China College of Computing & Data Science Nanyang Technology University Singapore

ISBN: (纸本)9798891760998

Advancing automated programming necessitates robust and comprehensive code generation benchmarks, yet current evaluation frameworks largely neglect object-oriented programming (OOP) in favour of functional programming (FP), e.g., HumanEval and MBPP. To address this, (1) our study introduces a pioneering OOP-focused benchmark, featuring 431 Python programs that encompass essential OOP concepts and features like classes and encapsulation methods. (2) We propose a novel evaluation metric, pass@o, tailored for OOP, enhancing traditional pass@k metric. (3) Our evaluation of 23 leading large language models (LLMs), including both general and code-specialized models, reveals three key insights: 1) pass@o offers a more relevant and comprehensive assessment for OOP code generation;2) Despite excelling in FP, code-specialized LLMs like WizardCoder lag in OOP compared to models like ChatGPT;3) The poor performance of all advanced LLMs on our OOP benchmark highlights a critical need for improvements in this field. Our benchmark and scripts are publicly released at: https://***/alphadl/OOP-eval. © 2024 Association for Computational Linguistics.

关键词： Object oriented programming

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Detecting Criminal Activities and Promoting Safety Using Deep Learning

Detecting Criminal Activities and Promoting Safety Using Dee...

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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Mathur, Rohan Chintala, Tejas Rajeswari, D. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu 603203 India

ISBN: (纸本)9781665495295

Automation and autonomous systems are among the few powerhouses of innovation that drive entire domains towards advancing further in leaps and bounds. Great technological innovations can be attributed to tasks that are made easier and more perceptible by automation, and artificial intelligence is here to make these automated systems smart enough to perform their tasks with the power of decision-making, thereby greatly reducing human intervention in redundant processes. Our project follows the aforementioned ideals: building a product to minimize manual labor (both physical and mental) for tasks that can be seamlessly automated and processed while solving the main problem statement at hand. Currently, surveillance cameras play a vital role to ensure the safety of the people, yet they are plain video-providing entities with no smart decision making mechanisms of their own. Because of this growth of data composed from surveillance cameras, automated video streams have become a requisite for automatically detecting abnormal events. The main aim of the project focuses on promoting safety on campus by employing deep learning techniques to automate the task of monitoring and reporting crimes from the physical Closed-Circuit Television (CCTV), assigning the responsibility of detecting criminal activity to a framework that can identify patterns to differentiate them for smarter monitoring. The model in this paper that we propose was able to distinguish between certain crimes with a 0.94 and 0.95 precision for Assault and Abuse respectively. © 2022 IEEE.

关键词： Crime

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Fuzzy Aggregator Based Energy Aware RPL Routing for IoT Enabled Forest Environment

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Computers, Materials & Continua 2022年第8期72卷 4039-4055页

作者： S.Srividhya Suresh Sankaranarayanan Sergei A.Kozlov Joel J.P.C.Rodrigues Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyChennai603203TamilnaduIndia Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyChennai603203TamilnaduIndia ITMO University St.PetersburgRussia Senac Faculty of Ceará FortalezaCEBrazil Instituto de Telecomunicações Covilhã6201-001Portugal

Forested areas are extremely vulnerable to disasters leading to environmental *** Fire is one among them which requires immediate *** are lot of works done by authors where Wireless Sensors and IoT have been used for forest fire ***,towards monitoring the forest fire and managing the energy efficiently in IoT,Energy Efficient Routing Protocol for Low power lossy networks(E-RPL)was *** were challenges about the scalability of the network resulting in a large end-to-end delay and less packet delivery which led to the development of Aggregator-based Energy Efficient RPL with data Compression(CAAERPL).Though CAA-ERPL proved effective in terms of reduced packet delivery,less energy consumption,and increased packet delivery ratio for varying number of nodes,there is still challenge in the selection of aggregator which is based purely on probability percentage of *** has been research work where fuzzy logic been employed for Mobile Ad-hoc Routing,RPL routing and cluster head selection in Wireless *** has been no work where fuzzy logic is employed for aggregator selection in Energy Efficient *** accordingly,we here have proposed Fuzzy Based Aggregator selection in Energy-efficient RPL for region thereby forming DODAG for communicating to Fog/*** here have developed fuzzy inference rules for selecting the aggregator based on strength which takes residual power,Node degree,and Expected Transmission Count(ETX)as input *** Fuzzy Aggregator Energy Efficient RPL(FA-ERPL)based on fuzzy inference rules were analysed against E-RPL in terms of scalability(First and Half Node die),Energy Consumption,and aggregator node energy *** the analysis,it was found that FA-ERPL performed better than *** were simulated using MATLAB and results.

关键词： Fuzzy logic aggregator IoT RPL routing wireless sensor network

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An Improved Detection of Skin Melanoma Using Hybrid CNN-MobileNet V2 Approach with Multi-Scale Attention

An Improved Detection of Skin Melanoma Using Hybrid CNN-Mobi...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： S.P. Maniraj R Thamizhamuthu Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Identifying melanoma in dermoscopic images is an immense challenge due to the varied appearances of the lesions. This research introduces a hybrid CNN-LSTM model augmented with a multi-scale attention mechanism to tackle this complexity, utilising CNNs for spatial feature extraction and LSTMs for temporal pattern recognition. Our model surpasses traditional CNN and MobileNet V2-LSTM models, with an accuracy of 96.98%, a recall of 96.65%, a specificity of 97.56%, and a JSI of 95.78 % . These measurements demonstrate enhanced sensitivity and reliability compared to baseline models, signifying strong diagnostic capabilities. This study improves melanoma detection, aiding in more precise diagnoses for clinical environments and remote telemedicine applications. Future initiatives involve enhancing the model for more comprehensive skin lesion categorisation.

关键词： Accuracy Sensitivity Attention mechanisms Telemedicine Melanoma Sensitivity and specificity Skin Pattern recognition Lesions Reliability

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S2AC: ENERGY-BASED REINFORCEMENT LEARNING WITH STEIN SOFT ACTOR CRITIC 12

S2AC: ENERGY-BASED REINFORCEMENT LEARNING WITH STEIN SOFT AC...

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12th International Conference on Learning Representations, ICLR 2024

作者： Messaoud, Safa Mokeddem, Billel Xue, Zhenghai Pang, Linsey An, Bo Chen, Haipeng Chawla, Sanjay Qatar Computing Research Institute Hamad Bin Khalifa University Qatar School of Computer Science and Engineering Nanyang Technological University Singapore SalesForce United States Skywork AI Data Science William & Mary United States

Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy Reinforcement Learning (MaxEnt RL), the policy is modeled as an expressive Energy-Based Model (EBM) over the Q-values. However, this formulation requires the estimation of the entropy of such EBMs, which is an open problem. To address this, previous MaxEnt RL methods either implicitly estimate the entropy, resulting in high computational complexity and variance (SQL), or follow a variational inference procedure that fits simplified actor distributions (e.g., Gaussian) for tractability (SAC). We propose Stein Soft Actor-Critic (S2AC), a MaxEnt RL algorithm that learns expressive policies without compromising efficiency. Specifically, S2AC uses parameterized Stein Variational Gradient Descent (SVGD) as the underlying policy. We derive a closed-form expression of the entropy of such policies. Our formula is computationally efficient and only depends on first-order derivatives and vector products. Empirical results show that S2AC yields more optimal solutions to the MaxEnt objective than SQL and SAC in the multi-goal environment, and outperforms SAC and SQL on the MuJoCo benchmark. Our code is available at: https://***/SafaMessaoud/S2AC-Energy-Based-RL-with-Stein-Soft-Actor-Critic. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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HelpBot: A Web-Based Chatbot to Handle Depression among Adolescents 24

HelpBot: A Web-Based Chatbot to Handle Depression among Adol...

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10th International Conference on computing and Artificial Intelligence, ICCAI 2024

作者： Jusoh, Shaidah Al Fawareh, Hejab Abdul Kadir, Rabiah Hosseinzadeh, Hassan School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Institute Visual Informatic Universiti Kebangsaan Malaysia Malaysia School of Health and Society University of Wollongong Australia

ISBN: (纸本)9798400717055

Depression is a pervasive mental health condition affecting millions of individuals including adolescents worldwide. In spite of the growing awareness of mental health issues, access to timely and personalized support remains a challenge. The advent of technology has offered new possibilities for providing support and assistance to those grappling with depression. This paper introduces a web-based chatbot called HelpBot. This chatbot is designed to be an innovative personal companion to offer personalized support, and encouragement to adolescents facing depression. By analyzing user's chat, the HelpBot tailors its responses and recommendations to suit individual needs. This personalized approach enhances the effectiveness of the support provided, offering a more meaningful and relevant experience for each user. The development of the HelpBot utilized .NET Framework technology, Natural Language Processing Technique (NLP) and Template based approach. The technology of .NET is used to create a dynamic web-based application, NLP technique is use to synthesize user's chat, and the template-based approach is used to offer a personalized response. © 2024 ACM.

关键词： A web-based chatbot

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Learning Hierarchical Spatial Tasks with Visiting Relations for Next POI Recommendation

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ACM Transactions on Recommender Systems 2023年第4期1卷 1-26页

作者： Nicholas Lim Bryan Hooi See-Kiong Ng Yong Liang Goh Renrong Weng Rui Tan GrabTaxi Holdings Singapore Institute of Data Science and School of Computing National University of Singapore

Sparsity is an established problem for the next Point-of-Interest (POI) recommendation task, where it hinders effective learning of user preferences from the User-POI matrix. However, learning multiple hierarchically related spatial tasks, and visiting relations between users and POIs, can help to alleviate this sparsity problem. In this article, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we propose a selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Further, we propose a novel Joint Triplet Loss Learning (JTLL) module to learn visited and unvisited relations between users and POIs for the recommendation task. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our proposed approach significantly outperforms baseline and the state-of-the-art methods.

关键词： Recommender system Graph Recurrent Network spatio-temporal joint learning triplet loss

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Diabetic Retinopathy Imaging: Network Exudate Detection to Classify Abnormalities

Diabetic Retinopathy Imaging: Network Exudate Detection to C...

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2023 Intelligent computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Chinnasamy, A. Selvakumari, P. School of Computing Srmist Department of Data Science and Business Systems kattankulathur campus Chennai India Chennai Institute of Technology Chennai Anna University Department of Computer Science and Engineering India

ISBN: (纸本)9798350394580

The DR becomes increasingly common, there is a need to automatically extract and classify disease severity. Due to diabetes problems, about 2% of people with this disease become completely blind, and DR complications make him 10% visually impaired if he has diabetes for 15 years. Furthermore, it plays an important role in the progression of blindness in middle-aged and older adults. If the disease is not recognized as soon as possible, patients can experience a severe stage of irreversible blindness. The shortage of ophthalmologists is a serious problem for the growing number of diabetic patients. Hard exudates must be found to screen and assist in disease monitoring and diagnosis. Therefore, Lloyd's clustering technique was used in this work. © 2023 IEEE.

关键词： Cat Swarm Optimization Diabetes Mellitus Diabetic Retinopathy Mean Squared Error

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